Selection of clusters based on internal indices in multi-clustering collaborative filtering recommender system
The successful application of a multi-clusteringbased neighborhood approach to recommender systems has led to increased recommendation accuracy and the elimination of divergence related to differences in clustering methods traditionally used. The Multi-Clustering Collaborative Filtering algorithm wa...
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2024-03-01
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| Series: | International Journal of Electronics and Telecommunications |
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| Online Access: | https://journals.pan.pl/Content/130649/PDF/13_4461_Kuzelewska_L_sk.pdf |
| _version_ | 1797242776137498624 |
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| author | Urszula Kużelewska |
| author_facet | Urszula Kużelewska |
| author_sort | Urszula Kużelewska |
| collection | DOAJ |
| description | The successful application of a multi-clusteringbased neighborhood approach to recommender systems has led to increased recommendation accuracy and the elimination of divergence related to differences in clustering methods traditionally used. The Multi-Clustering Collaborative Filtering algorithm was developed to achieve this, as described in the author’s previous papers. However, utilizing multiple clusters poses challenges regarding memory consumption and scalability. Not all partitionings are equally advantageous, making selecting clusters for the recommender system’s input crucial without compromising recommendation accuracy. This article presents a solution for selecting clustering schemes based on internal indices evaluation. This method can be employed for preparing input data in collaborative filtering recommender systems. The study’s results confirm the positive impact of scheme selection on the overall recommendation performance, as it typically improves after the selection process. Furthermore, a smaller number of clustering schemes used as input for the recommender system enhances scalability and reduces memory consumption. The findings are compared with baseline recommenders’ outcomes to validate the effectiveness of the proposed approach. |
| first_indexed | 2024-04-24T18:44:35Z |
| format | Article |
| id | doaj.art-ccacb57d8e6d4d1292c57078d08def94 |
| institution | Directory Open Access Journal |
| issn | 2081-8491 2300-1933 |
| language | English |
| last_indexed | 2024-04-24T18:44:35Z |
| publishDate | 2024-03-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | International Journal of Electronics and Telecommunications |
| spelling | doaj.art-ccacb57d8e6d4d1292c57078d08def942024-03-27T08:10:09ZengPolish Academy of SciencesInternational Journal of Electronics and Telecommunications2081-84912300-19332024-03-01vol. 70No 1Selection of clusters based on internal indices in multi-clustering collaborative filtering recommender systemUrszula KużelewskaThe successful application of a multi-clusteringbased neighborhood approach to recommender systems has led to increased recommendation accuracy and the elimination of divergence related to differences in clustering methods traditionally used. The Multi-Clustering Collaborative Filtering algorithm was developed to achieve this, as described in the author’s previous papers. However, utilizing multiple clusters poses challenges regarding memory consumption and scalability. Not all partitionings are equally advantageous, making selecting clusters for the recommender system’s input crucial without compromising recommendation accuracy. This article presents a solution for selecting clustering schemes based on internal indices evaluation. This method can be employed for preparing input data in collaborative filtering recommender systems. The study’s results confirm the positive impact of scheme selection on the overall recommendation performance, as it typically improves after the selection process. Furthermore, a smaller number of clustering schemes used as input for the recommender system enhances scalability and reduces memory consumption. The findings are compared with baseline recommenders’ outcomes to validate the effectiveness of the proposed approach.https://journals.pan.pl/Content/130649/PDF/13_4461_Kuzelewska_L_sk.pdfmulti-clusteringcollaborative filteringevaluation of clustering schemes |
| spellingShingle | Urszula Kużelewska Selection of clusters based on internal indices in multi-clustering collaborative filtering recommender system International Journal of Electronics and Telecommunications multi-clustering collaborative filtering evaluation of clustering schemes |
| title | Selection of clusters based on internal indices in multi-clustering collaborative filtering recommender system |
| title_full | Selection of clusters based on internal indices in multi-clustering collaborative filtering recommender system |
| title_fullStr | Selection of clusters based on internal indices in multi-clustering collaborative filtering recommender system |
| title_full_unstemmed | Selection of clusters based on internal indices in multi-clustering collaborative filtering recommender system |
| title_short | Selection of clusters based on internal indices in multi-clustering collaborative filtering recommender system |
| title_sort | selection of clusters based on internal indices in multi clustering collaborative filtering recommender system |
| topic | multi-clustering collaborative filtering evaluation of clustering schemes |
| url | https://journals.pan.pl/Content/130649/PDF/13_4461_Kuzelewska_L_sk.pdf |
| work_keys_str_mv | AT urszulakuzelewska selectionofclustersbasedoninternalindicesinmulticlusteringcollaborativefilteringrecommendersystem |